matten-data

Production-ready (0.45.x family). A scope-locked companion crate (RFC-033). The table-to-Tensor API (CSV ingestion, schema summary, column selection, missing-value handling, explicit numeric conversion) shipped in v0.20.1 (RFC-034, RFC-035) and was promoted to Beta in v0.22.0 (RFC-036), then to production-ready candidate in v0.27.0 (RFC-059), then to production-ready (RFC-085). The API is mostly stable but pre-1.0; pin the minor version.

Part of the matten workspace — see it for the full family.

Overview

matten-data is a tiny helper for the boring step between table-like input and a numeric matten::Tensor:

use matten_data::Table;

let csv = "sales,cost,note\n10,2,a\n20,,b\n30,4,c";
let tensor = Table::from_csv_str(csv)?
    .select_columns(["sales", "cost"])?   // pick numeric columns, by name
    .fill_missing(0.0)?                    // clean missing values explicitly
    .try_numeric()?                        // strict, explicit conversion
    .to_tensor()?;                         // -> matten::Tensor, shape [3, 2]

assert_eq!(tensor.shape(), &[3, 2]);
assert_eq!(tensor.as_slice(), &[10.0, 2.0, 20.0, 0.0, 30.0, 4.0]);
# Ok::<(), matten_data::MattenDataError>(())

Output shape is [rows, selected_columns]; row order is the input row order and column order is the requested selection order. See examples/ for a runnable version.

Not a dataframe library

matten-data is not a dataframe library. It deliberately has no joins, group-by, pivot, query DSL, lazy execution, indexing / loc / iloc, rolling or window operations, datetime engine, or categorical dtype system.

For dataframe or query workloads use Polars, DataFusion, Pandas, or another dataframe/query tool. matten-data is a small conversion helper for application-validated or trusted data, not a CSV firewall or malicious-input sandbox.

Streaming (optional, `streaming` feature)

CsvBatchReader (RFC-082) reads a CSV file in row-count-bounded Table batches, off by default behind the streaming feature — the csv feature is implied, no new dependency. This is a memory strategy, not a dataframe engine: no schema evolution, no lenient/skip-malformed mode, no streaming numeric conversion, and no async. A batch is exactly a Table; concatenating every batch reproduces Table::from_csv_path's output for ordinary input. Two malformed-input edge cases deliberately diverge from Table::from_csv_path (a file that is blank but not empty — one that trims to nothing but contains at least one whitespace character other than a line terminator, such as a space or tab — and invalid UTF-8) — see CsvBatchReader's own documentation for the exact behavior. This crate's production-ready promotion (RFC-085) covers the streaming feature too: stable in what it does — its two methods and their semantics are settled — but its scope may still grow (RFC-082 §5 defers nine further items, including async and resumability).

[dependencies]
matten-data = { version = "0.45.0", features = ["streaming"] }

use matten_data::CsvBatchReader;

# fn main() -> Result<(), matten_data::MattenDataError> {
# let path = std::path::Path::new("large.csv");
let mut reader = CsvBatchReader::open(path, 10_000)?;
while let Some(batch) = reader.next_batch()? {
    // process one Table batch at a time
}
# Ok(())
# }

Relationship to core `dynamic`

Core matten's dynamic feature is value-level ingestion (mixed values inside a Tensor, with explicit try_numeric()). matten-data is table-level preparation (headers, named columns, schema summary, table-shaped missing-value policy) whose end goal is a numeric Tensor. It may use core dynamic internally but does not expose a second computation engine.

Status and scope

Maturity: production-ready (RFC-085). The table-to-Tensor API is mostly stable but pre-1.0; pin the release explicitly.
Scope lock: table-to-Tensor preparation only (RFC-033, RFC-042).
Dependency direction: matten-data depends on core matten; core never depends on matten-data (enforced by the dependency-boundary CI check).
Safe Rust only: #![forbid(unsafe_code)].

Public API

The complete surface (the breaking-change baseline for this crate):

impl Table {
    pub fn from_csv_str(input: &str)                      -> Result<Table, MattenDataError>;
    pub fn from_csv_path<P: AsRef<Path>>(path: P)          -> Result<Table, MattenDataError>;
    pub fn row_count(&self)                                -> usize;
    pub fn column_count(&self)                             -> usize;
    pub fn column_names(&self)                             -> &[String];
    pub fn schema_summary(&self)                           -> SchemaSummary;
    pub fn select_columns<I, S>(&self, columns: I)         -> Result<Table, MattenDataError>;
    pub fn fill_missing(&self, value: impl Into<CellValue>) -> Result<Table, MattenDataError>;
    pub fn try_numeric(&self)                              -> Result<NumericTable, MattenDataError>;
}

impl NumericTable {
    pub fn row_count(&self)      -> usize;
    pub fn column_count(&self)   -> usize;
    pub fn column_names(&self)   -> &[String];
    pub fn to_tensor(&self)      -> Result<matten::Tensor, MattenDataError>;
}

// `streaming` feature only (off by default; implies `csv`) — RFC-082
impl CsvBatchReader {
    pub fn open(path: &Path, batch_rows: usize) -> Result<Self, MattenDataError>;
    pub fn next_batch(&mut self)                -> Result<Option<Table>, MattenDataError>;
}

pub enum CellValue { Text(String), Float(f64), Int(i64), Bool(bool), Missing }

#[non_exhaustive]
pub enum ColumnKind { Integer, Float, Boolean, Text, Mixed, MissingOnly }

pub struct ColumnSummary { pub name: String, pub kind: ColumnKind, pub missing: usize }

pub struct SchemaSummary {
    pub rows: usize,
    pub columns: usize,
    // per-column detail via column_summaries() -> &[ColumnSummary]
}

#[non_exhaustive]
pub enum MattenDataError {
    Csv { message: String },
    Io { path: PathBuf, source: std::io::Error },
    EmptyInput,
    MissingColumn { name: String },
    DuplicateColumn { name: String },
    DuplicateSelection { name: String },
    RaggedRow { row: usize, expected: usize, actual: usize },
    NonNumericValue { column: String, row: usize, value: String },
    MissingValue { column: String, row: usize },
    EmptySelection,
    InvalidBatchSize, // `streaming` feature only
    Matten(matten::MattenError),
}

Dependency style

This crate depends on matten. Official examples import Tensor from matten directly:

use matten::Tensor;
use matten_data::Table;

Declare both matten and this crate in your Cargo.toml (RFC-032).

Compatibility

SemVer: pre-1.0 (0.x). Released with the matten family version (RFC-030).
matten: the published manifest uses the workspace's broad pre-1.0 core requirement for maintenance (matten = "0", RFC-064); users should still declare the matched family explicitly.
MSRV: Rust 1.85 (edition 2024).

matten-data 0.45.0